Biostatistics Practice Questions

Q: The following data show systolic blood pressure (SBP) values and the number of patients corresponding to each value: SBP (mmHg) Salt intake 120 3.2 130 4.1 140 5.1 150 6.2

Scatter diagram. ***Scatter diagram*** - A **scatter diagram** (or scatter plot) is the most appropriate graphical method to represent the relationship between **two quantitative variables**, such as blood pressure and salt intake. - It allows visual assessment of the **correlation** or trend (positive, negative, or no correlation) between the two continuous variables. *Line graph* - A **line graph** is typically used to represent **trends over time** (e.g., mortality rates over different years). - It is not suitable for displaying the direct correlation between two distinct quantitative variables like systolic blood pressure and salt intake. *Ogive* - An **ogive** is used for plotting **cumulative frequency** distributions of quantitative data. - It helps in determining the **median** or quartiles, rather than showing independent relationships between two separate variables. *Pie chart* - A **pie chart** is used to represent **proportions or percentages** of a single categorical variable (e.g., distribution of blood types in a population). - It is entirely unsuitable for displaying the relationship between two continuous variables.

Q: A researcher studies serum cholesterol levels (continuous variable) in two independent groups of patients (100 patients in one group and 200 pat ients in another group). Which of the following statistical tests is most appropriate for analysis?

Unpaired t-test. ***Unpaired t-test*** - The **unpaired t-test** (or independent samples t-test) is the gold standard for comparing the **means** of a **continuous variable** between **two independent groups**. - Since the groups (n=100 and n=200) are unrelated to each other, this test is the statistically correct choice to determine if the difference in their cholesterol levels is **statistically significant**. *Paired t-test* - This test is utilized when comparing **repeated measurements** on the **same subjects** (e.g., pre-treatment vs. post-treatment values). - It is inappropriate here because the groups consist of distinct, unrelated individuals rather than the same patients measured twice. *Chi-square test* - The **Chi-square test** is used to analyze associations between **categorical** variables (e.g., presence or absence of a disease), not continuous data like cholesterol levels. - It assesses differences between **proportions** or frequencies rather than testing for differences in arithmetic means. *ANOVA* - **Analysis of Variance (ANOVA)** is designed to compare the means of **three or more** independent groups. - While technically it could yield similar results when applied to only two groups, the **unpaired t-test** is the standard, more specific, and appropriate choice for a two-group comparison.

Q: A new screening test is evaluated in a population of 1,000 individuals. Of these, 100 individuals are known to have the disease. The test correctly identifies 90 of the diseased individuals, while 50 non-diseased individuals are false positive. Based on the data below, what is the Positive Predictive Value (PPV) of this screening test? Screening Test Diseased Non-Diseased Test Positive 90 50 Test Negative 10 850 Total 100 900

64%. ***64%*** - The **Positive Predictive Value (PPV)** is calculated by the formula: **True Positives / (True Positives + False Positives)**. - Plugging in the values from the table: **90 / (90 + 50) = 90 / 140 ≈ 0.643**, which is approximately **64%**. *90%* - This represents the **Sensitivity** of the test, calculated as **True Positives / (True Positives + False Negatives)** (90 / 100). - It measures the ability of a test to correctly identify individuals who actually have the disease. *98%* - This calculation is incorrect and does not correspond to standard diagnostic test parameters. - A value this high would imply nearly perfect predictive capability, which is not supported by the data provided. *47%* - This value is likely obtained by calculating the **Negative Predictive Value (NPV)** incorrectly or using the wrong denominator. - It does not represent the probability that a person with a positive test result truly has the disease.

Q: Calculate the Odds Ratio with the following data: Cancer No Cancer Smoking 80 20 No Smoking 40 60

6. ***6*** - The **Odds Ratio (OR)** is calculated using the formula: (a * d) / (b * c), where (a) is exposed with disease, (b) is exposed without disease, (c) is unexposed with disease, and (d) is unexposed without disease. - Plugging in the values: (80 * 60) / (20 * 40) = 4800 / 800 = **6**, which represents the odds of developing **cancer** among smokers compared to non-smokers. *8* - This result would be obtained if the calculation was incorrect or if the **cross-multiplication** was mismatched during the division step. - It does not represent the correct mathematical ratio of the **exposure-disease association** provided in the table. *10* - This value likely arises from a calculation error, perhaps by substituting one of the denominators or numerators incorrectly, such as summing **incorrect cells**. - In **biostatistics**, ensuring the accurate placement of data in the **2x2 contingency table** is critical for deriving the correct odds. *12* - This result is mathematically incorrect and suggests a failure to properly apply the **odds ratio formula** to the provided contingency table data. - An OR of 12 would imply a much stronger **association** than what is actually supported by the raw frequencies of smoking and cancer incidence in this study group.

Question 1

The following data show systolic blood pressure (SBP) values and the number of patients corresponding to each value: SBP (mmHg) Salt intake 120 3.2 130 4.1 140 5.1 150 6.2

Accepted Answer

Scatter diagram

Answer

Line graph

Answer

Ogive

Answer

Pie chart

Question 2

A researcher studies serum cholesterol levels (continuous variable) in two independent groups of patients (100 patients in one group and 200 pat ients in another group). Which of the following statistical tests is most appropriate for analysis?

Accepted Answer

Unpaired t-test

Answer

Paired t-test

Answer

Chi-square test

Answer

ANOVA

Question 3

Which statement is correct regarding assessment of heterogeneity in meta-analysis?

Accepted Answer

It evaluates the degree of variability in results across the combined studies

Answer

It directly quantifies and corrects for publication bias

Answer

It automatically removes confounding variables from individual studies

Answer

It computes the incidence rate of the outcome across all populations

Question 4

A new screening test is evaluated in a population of 1,000 individuals. Of these, 100 individuals
are known to have the disease. The test correctly identifies 90 of the diseased individuals, while 50
non-diseased individuals are false positive. Based on the data below, what is the Positive
Predictive Value (PPV) of this screening test?
Screening Test Diseased Non-Diseased
Test Positive 90 50
Test Negative 10 850
Total 100 900

Accepted Answer

64%

Answer

90%

Answer

98%

Answer

47%

Question 5

Calculate the Odds Ratio with the following data:

Cancer No Cancer
Smoking 80 20
No Smoking 40 60

Accepted Answer

6

Answer

8

Answer

10

Answer

12

Biostatistics — MCQs

Biostatistics — MCQs

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